Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory290.1 KiB
Average record size in memory100.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 5 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_productsHigh correlation
qtde_returns is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279) Skewed
frequency is highly skewed (γ1 = 24.88037069) Skewed
qtde_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-11-22 01:14:37.804514
Analysis finished2022-11-22 01:15:50.220824
Duration1 minute and 12.42 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:50.577025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2022-11-21T22:15:50.959960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2022-11-21T22:15:51.623730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2022-11-21T22:15:51.999414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.226056
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:52.446138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.4905
Coefficient of variation (CV)3.848534202
Kurtosis353.9585684
Mean2749.226056
Median Absolute Deviation (MAD)672.72
Skewness16.77787915
Sum8162452.16
Variance111946779.3
MonotonicityNot monotonic
2022-11-21T22:15:52.814914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
889.932
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2944)2949
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28864938
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:53.196678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75617089
Coefficient of variation (CV)1.209485215
Kurtosis2.778038567
Mean64.28864938
Median Absolute Deviation (MAD)26
Skewness1.798396863
Sum190873
Variance6046.022112
MonotonicityNot monotonic
2022-11-21T22:15:53.585439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.72280229
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:54.436915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85665393
Coefficient of variation (CV)1.547607882
Kurtosis190.8253633
Mean5.72280229
Median Absolute Deviation (MAD)2
Skewness10.76645634
Sum16991
Variance78.44031883
MonotonicityNot monotonic
2022-11-21T22:15:54.804442image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.461098
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:55.190206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.976527
Coefficient of variation (CV)3.6620722
Kurtosis467.153716
Mean1606.461098
Median Absolute Deviation (MAD)420
Skewness17.87844459
Sum4769583
Variance34609412.81
MonotonicityNot monotonic
2022-11-21T22:15:55.623746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2608
 
0.3%
848
 
0.3%
2888
 
0.3%
2728
 
0.3%
2468
 
0.3%
5167
 
0.2%
3947
 
0.2%
Other values (1655)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.705288
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:56.041487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8419967
Coefficient of variation (CV)2.199106503
Kurtosis354.8373546
Mean122.705288
Median Absolute Deviation (MAD)44
Skewness15.70613971
Sum364312
Variance72814.70321
MonotonicityNot monotonic
2022-11-21T22:15:56.436247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2933
 
1.1%
1933
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2529
 
1.0%
Other values (459)2630
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
56701
< 0.1%
50951
< 0.1%
45771
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.90005685
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:56.831585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.97438356
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934336
Coefficient of variation (CV)19.9794451
Kurtosis2890.70744
Mean51.90005685
Median Absolute Deviation (MAD)5.994222271
Skewness53.4442279
Sum154091.2688
Variance1075232.818
MonotonicityNot monotonic
2022-11-21T22:15:57.189365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2956)2956
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35143043
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:57.595543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92857143
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.4047619

Descriptive statistics

Standard deviation63.54282948
Coefficient of variation (CV)0.9434518178
Kurtosis4.887703174
Mean67.35143043
Median Absolute Deviation (MAD)26.28571429
Skewness2.062908983
Sum199966.397
Variance4037.691178
MonotonicityNot monotonic
2022-11-21T22:15:57.970179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
116
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137912226
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:58.362931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008894164194
Q10.01633986928
median0.02588996764
Q30.04941860465
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03307873537

Descriptive statistics

Standard deviation0.4081571514
Coefficient of variation (CV)3.586894861
Kurtosis989.3578171
Mean0.1137912226
Median Absolute Deviation (MAD)0.0121913375
Skewness24.88037069
Sum337.8461398
Variance0.1665922603
MonotonicityNot monotonic
2022-11-21T22:15:58.729705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.0277777777817
 
0.6%
0.062517
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2637
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.1569552
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:59.131458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)24.33349783
Kurtosis2765.52864
Mean62.1569552
Median Absolute Deviation (MAD)1
Skewness51.79774426
Sum184544
Variance2287644.557
MonotonicityNot monotonic
2022-11-21T22:15:59.513096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
678
 
2.6%
561
 
2.1%
1251
 
1.7%
843
 
1.4%
743
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
01481
49.9%
1164
 
5.5%
2148
 
5.0%
3105
 
3.5%
489
 
3.0%
561
 
2.1%
678
 
2.6%
743
 
1.4%
843
 
1.4%
941
 
1.4%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%
20121
< 0.1%
17761
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.349541
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:15:59.934670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.5024106
Coefficient of variation (CV)3.174268569
Kurtosis2256.245507
Mean249.349541
Median Absolute Deviation (MAD)82.75
Skewness44.68328098
Sum740318.7873
Variance626476.066
MonotonicityNot monotonic
2022-11-21T22:16:00.339436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
869
 
0.3%
829
 
0.3%
1368
 
0.3%
608
 
0.3%
758
 
0.3%
888
 
0.3%
717
 
0.2%
Other values (1963)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1010
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.15507374
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-21T22:16:00.765190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.345454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.51303316
Coefficient of variation (CV)0.8807478316
Kurtosis27.69469772
Mean22.15507374
Median Absolute Deviation (MAD)8.2
Skewness3.498252107
Sum65778.41393
Variance380.7584629
MonotonicityNot monotonic
2022-11-21T22:16:01.126732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353
 
1.8%
1440
 
1.3%
1138
 
1.3%
2033
 
1.1%
933
 
1.1%
132
 
1.1%
1831
 
1.0%
1030
 
1.0%
1629
 
1.0%
1728
 
0.9%
Other values (1000)2622
88.3%
ValueCountFrequency (%)
132
1.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
224
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1271
< 0.1%
1221
< 0.1%
1181
< 0.1%

Interactions

2022-11-21T22:15:43.778045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:48.171623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:53.360009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:57.984006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:02.305146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:06.900801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:11.012343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:15.606870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:20.620768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:24.865539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:29.570734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:34.434426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:39.119568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:44.098847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:48.781996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:53.725785image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:58.314822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:02.621953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:07.219605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:11.355149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:15.942539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:20.952571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:25.216874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:29.878396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:34.755866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:39.463676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:44.515589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:49.339654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:54.037579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:58.647329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:03.165565image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:07.514433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:11.708572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:16.555448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:21.272836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:25.681869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:30.225183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:35.101652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:39.802464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:45.032154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:49.684441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:54.372775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:58.945146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:03.478367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:07.799274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:12.034401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:16.889729image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:21.586671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:26.017576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:30.561978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:35.426268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:40.132260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:45.372963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:50.028229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:54.696964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:59.269946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:03.812213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:08.109111image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:12.392202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:17.242280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:21.923023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:26.377294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:30.892773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:35.771056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:40.488041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:45.666803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:50.371018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:55.005770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:59.555778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:04.108794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:08.389949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:12.729021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:17.587068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:22.223854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:26.685337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:31.192587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:36.086860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:40.805453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:46.030862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:50.730796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:55.354473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:59.900568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:04.469569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:08.721262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:13.101323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:17.945094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:22.586138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:27.053742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:31.549877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:36.507601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:41.166231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:46.487594image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:51.080580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:55.691265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:00.251377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:04.831352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:09.040573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:13.464117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:18.316865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:22.926940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:27.399335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:31.917650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:36.893363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:41.551701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:46.805335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:51.618770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:56.027091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:00.566864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:05.128181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:09.339400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:13.787440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:18.649657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:23.228547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:27.708147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:32.245733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:37.221164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:41.904502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:47.162083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:51.958129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:56.528780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:00.899362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:05.480782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:09.692234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:14.159254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:18.987472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:23.562349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:28.097906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:32.615788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:37.580991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:42.268105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:47.501905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:52.291921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:56.960516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:01.236156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:05.817576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:10.034398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:14.530022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:19.431223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:23.894153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:28.489665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:32.978568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:37.943913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:42.629881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:47.874696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:52.642681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:57.305305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:01.631926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:06.197351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:10.387202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:14.900266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:19.816257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:24.227972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:28.869159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:33.365326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:38.396162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:43.012676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:48.225477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:53.002751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:14:57.652107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:01.972720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:06.576000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:10.709354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:15.266068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:20.197052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:24.560298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:29.227935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:34.091619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:38.779687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-21T22:15:43.437438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-21T22:16:01.464524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-21T22:16:02.000243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-21T22:16:02.684860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-21T22:16:03.244525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-21T22:16:03.920130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-21T22:15:48.753349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-21T22:15:49.879239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705888.735294
11130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444419.000000
22125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.20000015.466667
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666671.000000
55152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428577.285714
66146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.42857115.571429
77178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166675.083333
881531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.71428626.142857
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.0
2960563717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
2961563817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
2962564913596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
29635655148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
2964565912479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.0
2965568014126706.137.03.0508.015.047.0753333.00.75000050.0169.3333335.0
29665686135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
2967569615060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
2968571512558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.0